Sanjeev Chowdhri - Senior Product Manager, Analytics Lu Liu - Analytics Consultant SunGard Energy Solutions

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1 Mr. Chowdhri is responsible for guiding the evolution of the risk management capabilities for SunGard s energy trading and risk software suite for Europe, and leads a team of analysts and designers in Houston, USA and Pune, India. Ms. Liu s main area of expertise is in advanced analytics, specifically in the identification and quantification of financial and physical risk exposures in energy trading portfolios. Effective Management of Complex Structured Contracts Sanjeev Chowdhri - Senior Product Manager, Analytics Lu Liu - Analytics Consultant SunGard Energy Solutions Copyright 2006 SunGard. Trademark Information: SunGard and the SunGard logo and are registered trademarks of SunGard Data Systems Inc. in the U.S. and other countries.

2 Effective Management of Complex Structured Contracts As natural gas and electricity markets throughout Europe go through a period of deregulation and become more mature, market participants are trading not only financially-settled futures or the simplest physicals, but also complex contracts, which cover multiple locations and asset classes. Long-term bilateral energy contracts often exhibit a complexity of price averaging, and customised optionality clauses. Structured trades combine several trading instruments to meet specific risk management or trading objectives. In addition, more complicated derivative instruments have evolved in accordance with the demands of energy market participants. From an energy company s point of view, the optionality within these structured contracts is highly valuable, but at the same time presents considerable risk. This paper provides you with a framework of how to identify, measure and manage various risk exposures in complex structured contracts in order to maximise their value in energy portfolios. Identify and Measure the Risk As an energy market participant, your challenge is to effectively manage common complex structured contracts including swing, spread, and rainbow options, together with gas storage and power generation assets. The importance of each element to your portfolio will now be discussed in turn. Swing options are prevalent in long-term contracts with the existence of swing features as embedded options within these contracts for many years. Swing features consist of a series of interrelated agreements to purchase a flexible quantity of an energy commodity over a specific period of time at a predetermined price. The swing element of a gas contract can be considered as a collection of individual options, and therefore presents significant value. However, before the techniques to value options were developed, these embedded options were mis-priced. Today, swing options can be accurately valued, which makes it possible to quantify the benefit of this optionality. In addition, the swing option itself can be used as a tool to manage volumetric risk. This further increases the importance of valuing swing options as part of your entire portfolio. A good ETRM system can help you make the most of a swing option s value potential in several ways. It can provide exercise decision support, track exercise decisions, track contract lifetime constraints, and integrate swing exercise with scheduling. The importance of good invoicing over multi-year contracts should also be recognised and incorporated into your whole risk management process. Contracts contain a number of complicated provisions and penalty terms as well as quantity formulae for setting tolerances on delivered volumes. Inadequate invoicing procedures for long-term contracts can lead to the unnecessary loss of huge amounts of money, which can be avoided. There are several approaches to value swing contracts, including tree methods, Monte Carlo simulation, and closed form solutions derived from the Black-Scholes formula. SunGard recommends the use of dynamic stochastic programming methodology based on trinomial trees to value your swing options. Spread options are the most traded instruments in the world of energy trading as most market participants have exposures across various types of commodities. These options include crack and spark spreads (the spread between two commodities), and calendar spreads (differential between futures contracts with different maturities). As the profit of a gas-fired electricity generation asset is largely dependent on the spread between price of natural gas and electricity, spread options are widely used in the valuation of power plants, oil refineries, storage facilities and transmission lines. A good ETRM system can help you value spread options and help you to better understand the value of your gas storage and power generation assets. Rainbow options link two or more underlying assets across multiple locations and different asset classes. The most popular rainbow option is a basket option, where the payoff is dependent on the value of a portfolio of assets. Rainbow

3 options combined with spread options can be used to value a fuel-switching generator, which allows you to compare the spreads between different commodity combinations e.g. electricity/gas, electricity/coal in different locations. To help you value rainbow options, SunGard uses the widely regarded Monte Carlo simulation method. In addition to the effective management of these contracts that are specifically being traded, the efficient operation and accurate valuation of storage facilities and power generation assets are equally as important considerations. Gas storage - From a financial point of view, the real option value of gas storage assets or contracts comes from the flexibility to choose between injecting, storing, or withdrawing gas from storage during a specified exercise period. Flexibility is typically limited by a number of constraints, such as the maximum injection and withdrawal rates, maximum and minimum storage levels, injection and withdrawal costs. The value of storage is derived from maximizing the expected return from operating the storage facility against these specified constraints. Three methods can be used to value a gas storage facility. Given the forecasted spot price for gas, storage value can be calculated via dynamic programming, similar to deterministic dynamic programming methodology used in the valuation of a swing option. A gas storage facility can also be calculated as a strip of European calendar spread options over time. Finally, a trinomial tree approach is favourable, as it takes into account the asset s dynamic constraints, interdependence of exercise decisions and price uncertainty. Power Generation Assets provide plant owners with return on investment through the mechanism of converting fuel into electricity at a technology dependent conversion rate - the heat rate, which summarizes the amount of fuel that a particular generation asset requires to generate a given amount of electricity. The operator has the flexibility to ramp up, stay still or ramp down the output of the generator. It is the value of this optionality which decides the value of the generation asset during a specific period. Like the gas storage facility, the generation asset is subject to a number of constraints such as maximum power output, maximum ramp up/down rate and minimum online/offline time. The relationship between the value of spark spread options and the value of generation assets can also be considered as a method to identify potential hedge opportunities for the owners of generation assets. Furthermore, the introduction of emissions compliance and trading increase the challenge for efficient operation of generation assets. To address the challenges of valuing power generation, SunGard provides a power generation option valuation model, which is based on a multilevel trinomial tree method. This is designed to provide you with decision support for the optimal operation of your generation assets together with their valuation and trading. The model is designed to stress the impact of operation costs and constraints on the operator s profitability, incorporate price uncertainties in the fuel and electricity market, and use an optimization algorithm to derive optimal dispatch strategies. Incorporating Structured Contracts Within a Global Risk Management Framework Although the number of structured contracts within an energy company s portfolio is relatively low, they represent a disproportionately high impact on the asset owner s profit and risk. Energy market participants today face immense challenges of managing a relatively small number of complex contracts without losing the view of the entire portfolio. Therefore, a company s global risk reporting process incorporating MtM, P&L, and VaR should also be applied to structured contracts in order to manage price and volumetric risk. A good ETRM system will help you to conduct risk reporting calculations for all types of structured contracts as part of the total portfolio, covering a wide range of risks, such as nonlinear price risk and volatility risk. The system should also be flexible enough to calculate VaR without assuming specific distribution of underlying risk factors. Value-at-Risk (VaR) summarizes the worst expected loss that an institution can suffer over a given time interval under normal market conditions at a given level of confidence. The use of VaR has become the standard for risk reporting

4 within the energy industry. Monte-Carlo simulation has become the method of choice for its calculation because it incorporates time varying volatility and extreme scenarios. To help you to incorporate complex structured trades in your risk reporting calculations, SunGard provides VaR methodology based on the Monte-Carlo method. This simulates forward price curves associated with base curves for specified markets and components. Our Monte-Carlo engine can be directly applied to physical deals as well as all types of structured contracts in these calculations. SunGard s ETRM system affords great flexibility in allowing the separation of option valuation models from the Monte Carlo simulation, such that swing and rainbow options, storage and generation assets are re-valued in each scenario against the new forward price curves. This allows risk reporting calculations for the complex structured trades to be carried out following the same logic as for simple physical trades. Earnings-at-Risk (EaR) is another milestone in risk management after VaR and is today widely used by corporations. EaR allows you to monitor earnings for a predetermined time so that you can react to unfavourable price movements. SunGard s EaR calculation works in conjunction with the VaR calculation, by simulating spot prices on top of the forward price dynamics. However, it should be noted that VaR measures based on normal market conditions can fail to identify extreme unusual situations that could cause severe losses. Therefore, VaR methods need to be supplemented by stress testing. Stress testing examines the effect of simulated large movements in key financial variables in the portfolio. Stress testing allows you to specify scenarios of interest to assess possible changes in the value of the portfolio. Process Flow for Effective Management of Complex Structured Contracts

5 Conclusion Risk will never be completely eliminated and the plethora of complex structured contracts creates even more complication for energy market participants. Complex structured contracts themselves are used as tools for managing price risk and volumetric risk. These contracts represent a sizeable portion of energy portfolios and bring enormous value for companies. Yet at the same time they create additional risks that need to be identified, measured and managed. A fully integrated approach is required to accomplish the effective management of complex structured contracts, encompassing deal capture, valuation and decision support. Furthermore, risk reporting should also include these complex structured trades as an integral part of the entire portfolio. Although risk management has evolved over time, techniques for identifying, measuring and controlling various risks remain unchanged. There are multiple ways in which VaR figures can be derived, all methods achieve the same result, but each works in a different way, and each has its advantages and disadvantages. A good ETRM system will incorporate complex structured trades within the overall risk management framework, and be flexible enough to take into account different portfolio structures, varying levels of risk tolerance and the organisation s specific IT environment.